Sudden 'incompatible shapes' when switching dataset

Hey folks, I have never really posted anything online but at the moment, I’m all out of ideas.

The thing is, I am trying to run FISM (a classifier or recommender system algorithm) — specifically, this implementation: GitHub - yushuai/FISM: implementation for the paper "FISM: Factored Item Similarity Models for Top-N Recommender Systems" by Tensorflow 1.2. When I apply it to the provided data set (dataset 1: ml_train & ml_test), it works. When I apply it to dataset 2, which I processed myself so that the format is equal to dataset 1, it also works like expected. But, when I apply it to dataset 3, which also has equal formatting, I get an error in tf.multiply: Incompatible shapes: [99,1] and [100,64].

It is so strange to me that the code works for datasets 1 and 2, but not for 3. I have checked the entire dataset and there is nothing out of the ordinary. I have also scoured the internet for answers, but I haven’t been able to find any. So, here’s my plea for help. This particular part is crucial for the success of my research…

I have already tried reducing the size of dataset 3 but to no avail.

Does anyone know why this error is occurring?


Traceback (most recent call last):
File “C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\client\”, line 1365, in _do_call
return fn(*args)
File “C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\client\”, line 1350, in _run_fn
target_list, run_metadata)
File “C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\client\”, line 1443, in _call_tf_sessionrun
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [99,1] vs. [100,64]
[[{{node Mul}}]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File “”, line 194, in
File “”, line 103, in train
hit_ratio, ndcg = self.evaluate(sess)
File “”, line 163, in evaluate
self.neighbour_num: [neighbour_number for _ in x_test]
File “C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\client\”, line 956, in run
File “C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\client\”, line 1180, in _run
feed_dict_tensor, options, run_metadata)
File “C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\client\”, line 1359, in _do_run
File “C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\client\”, line 1384, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [99,1] vs. [100,64]
[[node Mul (defined at C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\framework\ ]]

Original stack trace for ‘Mul’:
File “”, line 193, in
File “”, line 130, in build_graph
user_repr = tf.multiply(inverse_rated_num, sumvec)
File “C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\util\”, line 180, in wrapper
return target(*args, **kwargs)
File “C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\ops\”, line 331, in multiply
return gen_math_ops.mul(x, y, name)
File “C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\ops\”, line 6701, in mul
“Mul”, x=x, y=y, name=name)
File “C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\framework\”, line 794, in _apply_op_helper
File “C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\util\”, line 507, in new_func
return func(*args, **kwargs)
File “C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\framework\”, line 3357, in create_op
attrs, op_def, compute_device)
File “C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\framework\”, line 3426, in _create_op_internal
File “C:\Users\semye\anaconda3\envs\py3.5\lib\site-packages\tensorflow_core\python\framework\”, line 1748, in init
self._traceback = tf_stack.extract_stack()

Hi @semple_y,

Welcome to the TensorFlow Forum!

The given information is not enough to replicate the error. Could you please share minimal reproducible code to replicate and to understand the issue for fixing it?

Thank you.